Results 11 to 20 of about 220,848 (275)
A Review of Optical Neural Networks
IEEE Access, 2020 Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has been limited by Moore's law.
Xiubao Sui +4 more
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Large-Scale Optical Neural Networks based on Photoelectric Multiplication
Physical Review X, 2019 Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large ($N ...
Bernstein, Liane +4 more
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Pluggable multitask diffractive neural networks based on cascaded metasurfaces
Opto-Electronic AdvancesOptical neural networks have significant advantages in terms of power consumption, parallelism, and high computing speed, which has intrigued extensive attention in both academic and engineering communities.
Cong He +10 more
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Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks. [PDF]
IEEE J Sel Top Quantum Electron, 2020 Mengu D, Luo Y, Rivenson Y, Ozcan A.
europepmc +2 more sources
Single-shot optical neural network
Science Advances, 2023 Analog optical and electronic hardware has emerged as a promising alternative to digital electronics to improve the efficiency of deep neural networks (DNNs). However, previous work has been limited in scalability (input vector lengthK≈ 100 elements) or has required nonstandard DNN models and retraining, hindering widespread adoption.
Liane Bernstein +5 more
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Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits
Photonics, 2021 We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation.
Qi Zhang, Zhuangzhuang Xing, Duan Huang
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Opto-Electronic Hybrid Network Based on Scattering Layers
Sensors, 2023 Owing to the disparity between the computing power and hardware development in electronic neural networks, optical diffraction networks have emerged as crucial technologies for various applications, including target recognition, because of their high ...
Jiakang Zhu +4 more
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Adaptive Photochemical Nonlinearities for Optical Neural Networks
Advanced Intelligent Systems, 2023 Optical neural networks (ONNs) hold great potential for faster and more energy‐efficient information processing in coherent photonic circuits. To realize ONNs, linear combinations and nonlinear activation functions have to be implemented in an optical ...
Marlon Becker +5 more
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A Review of Optical Neural Networks
Applied Sciences, 2022 With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and ...
Danni Zhang, Zhongwei Tan
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Optical Soliton Neural Networks
, 2023 The chapter describes the realization of photonic integrated circuits based on photorefractive solitonic waveguides. In particular, it has been shown that X-junctions formed by soliton waveguides can learn information by switching their state. X junctions can perform both supervised and unsupervised learning.
Eugenio Fazio +2 more
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